Biology subjects

Majoros, W. H.

Publications and source records attributed to Majoros, W. H..

10 recordsLinked to original sources

bioRxiv2026

Probabilistic mapping of sub-genic intolerance reveals functional and disease-critical protein regions

Different regions of genes perform distinct functions and vary in their importance to human health. Evolutionary intolerance provides a powerful means of identifying regions where disruptive mutations are under strong purifying selection, informing genetic disease discovery and variant interpretation. However, estimating intolerance in small sub-genic regions from population variation alone is underpowered and unstable. We present PRIME, a Bayesian model that stabilizes estimates of regional missense intolerance by sharing information hierarchically across regions. Importantly, PRIME produces a full joint posterior across all genes, allowing complex inferential questions that are difficult or impossible to address with existing approaches to be answered. We utilize this to identify regions enriched for pathogenic and experimentally deleterious missense variants, improve prioritization of Mendelian disease genes by focusing on their most intolerant regions, and uncover conserved patterns of purifying selection across protein families. Integrating PRIME with existing computational variant predictors improves pathogenicity prediction, demonstrating that regional missense intolerance provides complementary information for clinical variant interpretation.

genomics↗

bioRxiv2026

A Bayesian Multi-Species Approach Infers Gene Regulatory Networks Across Non-Model Organisms

Control of gene expression by transcription factors (TFs) is a critical mechanism for cells to maintain homeostasis in response to environmental signals. Gene network models that predict regulatory interactions between transcription factors and the genes they control aid in understanding these complex processes. These models are useful as they provide testable hypotheses of regulatory interactions, transcription factor function, and accelerate the study of uncharacterized transcription factors. However, inference of these models is computationally challenging due to the vast quantity of data required given the many possible states of the regulatory network. Microbial genomes encode hundreds of transcription factors, with numerous interactions that require substantial functional genomics datasets to infer. This problem is accentuated in understudied organisms, species that would greatly benefit from an inferred network for biological discovery, where the lack of available data is particularly constraining for effective inference. To address this problem, we have developed GRN-BMuSeR (Gene Regulatory Networks from Bayesian MUlti-SpEcies Regression), a novel multitask approach to gene regulatory network inference that leverages gene orthology between closely related species to improve inference performance. We evaluate its performance on a dataset from the well-studied bacterial species Bacillus subtilis, demonstrating improved performance in multitask settings. Applying the model to simulated data reveals utility in multi-species contexts. Finally, we apply our models to infer GRNs and explore predictions for two hypersaline-adapted archaeal species. We leverage a rich dataset from Halobacterium salinarum to inform the inference of the gene regulatory network of Haloferax volcanii, for which a more limited genomics dataset was available. We generate a large compendium of gene expression data for Hfx.volcanii for GRN inference input. Through exploration of resultant network predictions, we show concordance with known TF functions and discover hundreds of novel TF functional predictions. Moving forward, our results provide a framework to generate testable hypotheses that will serve to guide experimental work and accelerate discovery in these understudied species.

systems biology↗

bioRxiv2026

Modeling gene regulatory perturbations via deep learning from high-throughput reporter assays

Assessing likely variant effects on phenotypes is of critical importance in diagnostic settings, and while much progress has been made in interpreting genic mutations based on our understanding of coding sequence, noncoding variants can be much more challenging to reliably interpret based on DNA sequence alone. High-throughput reporter assays such as STARR-seq and MPRA have shown utility in experimentally measuring regulatory effects of noncoding variants present in samples but provide no readout for variants not present in the assay inputs. However, whole-genome reporter assays provide copious data that can be used to train predictive models for prioritizing variants not directly observed in the experiment. We describe a retrainable predictive modeling framework, BlueSTARR, for this task, and present results of training several models with this framework on whole-genome STARR-seq data from two cell lines and one drug treatment. Using these models, we uncover a global signature across the human genome consistent with purifying selection against both loss-of-function and gain-of-function regulatory variants, with the latter showing a significant bias consistent with selection against gains of cis regulatory function in closed chromatin proximal to genes. By testing the model on synthetic enhancers with binding motifs for transcription factors GR and AP-1, we find that when trained on drug perturbation data, the model is able to learn distance-dependent and treatment-dependent binding patterns and their resulting reporter gene activation. These results demonstrate that lightweight, easily retrainable models such as ours have utility in probing latent signals present in novel experimental data. Finally, we find only modest differences in performance between different deep-learning architectures when trained on this single data modality, and while somewhat greater predictive accuracy can be achieved with much larger models trained at great expense on many terabytes of data, there is still copious room for improvement even for industrial strength, state-of-the-art models.

bioinformatics↗

bioRxiv2026

Structured Pooling Improves Detection of Rare Regulatory Mutations in Population-Scale Reporter Assays

Identifying genetic variants in noncoding DNA that impact gene expression and thereby contribute to disease risk remains a difficult but important challenge in genomic medicine. Modern reporter assays such as STARR-seq and MPRA provide an efficient and effective means of testing, in very high throughput, millions of variants captured directly from patient genomes. While these assays have previously been scaled to whole genomes and, separately, to populations, we report findings from the first whole-genome population-scale STARR-seq experiment performed on 100 individuals. In order to achieve that scale we devised a novel experimental design that partitions samples into pools so as to increase allele frequencies within pools and thereby reduce expected dropout and increase signal-to-noise ratio in experimental readouts. We show that this design produces more accurate estimates of variant effect sizes, and we provide a Bayesian model for robust estimation of those effect sizes that also reports full posterior distributions for assessment of confidence in estimates. Together, these methodological innovations facilitate the detection of functional regulatory variants, particularly rare variants, with much higher accuracy and at greater scale than previously possible. We demonstrate the utility of this approach on the task of functional annotation of quantitative trait loci such as eQTLs and caQTLs, and show concordance with patterns of constraint in transcription factor binding profiles.

bioinformatics↗

bioRxiv2026

Identifying Inheritance Patterns of Allelic Imbalance, using Integrative Modeling and Bayesian Inference

Interpreting the effects of novel mutations on phenotypic traits remains challenging, particularly for cis-regulatory variants. For rare variants, individuals typically possess at most one affected copy of the causal allele, leading to allelic imbalance, and thus the ability to infer inheritance of allelic imbalance can inform genetic studies of phenotypic traits. While many methods for detection of allele-specific expression (ASE) exist, they largely focus on ASE in one individual. We show that performing joint inference across multiple individuals in a trio allows for simultaneously improving estimates of ASE and identifying its likely mode of inheritance. Our Bayesian approach has the benefit of being able to (1) aggregate information across individuals so as to improve statistical power, (2) estimate uncertainty in estimates, and (3) rank modes of inheritance by posterior probability. We demonstrate that this model is also applicable to other forms of imbalance such as allele-specific chromatin accessibility. Applying the model to ATAC-seq and RNA-seq from several trios, we uncover examples in which ASE can be linked to imbalance in chromatin state of cis-regulatory elements and to potential causal variants. As the cost of sequencing continues to decrease, we expect that powerful methodologies such as the one presented here will promote more routine collection of samples from related individuals and improve our understanding of genetic effects on gene regulation and their contribution to phenotypic traits.

bioinformatics↗

bioRxiv2025

Cell modeling and rescue of a novel non-coding genetic cause of Glycogen Storage Disease IX

Delayed diagnosis of Mendelian disease substantially prevents early therapeutic intervention that could improve symptoms and prognosis. One major contributing challenge is the functional interpretation of non-coding variants that cause disease by altering splicing and/or gene expression. We identified two siblings with glycogen storage disease (GSD) type IX {gamma}2, both of whom had a classic clinical presentation, enzyme deficiency, and a known pathogenic splice acceptor variant on one allele of PHKG2. Despite the autosomal recessive nature of the disease, no variant on the second allele was identified by gene panel sequencing. To identify a potential missing second pathogenic variant, we completed whole genome sequencing (WGS) and detected putative deep intronic splicing variant in PHKG2 in both siblings. We confirmed the functional splicing effects of this variant using short-read and long-read RNA-seq on patient blood and a HEK293T cell model in which we installed the variant using CRISPR editing. Using the cell model, we demonstrated multiple biochemical and cellular impacts that are consistent with GSD IX {gamma}2, and a reversal of aberrant splicing using antisense splice-switching oligonucleotides. In doing so, we demonstrate a novel and robust pathway for detecting, validating, and reversing the impacts of novel non-coding causes of rare disease.

genetics↗

bioRxiv2024

Characterization and bioinformatic filtering of ambient gRNAs in single-cell CRISPR screens using CLEANSER

Recent technological developments in single-cell RNA-seq CRISPR screens enable high-throughput investigation of the genome. Through transduction of a gRNA library to a cell population followed by transcriptomic profiling by scRNA-seq, it is possible to characterize the effects of thousands of genomic perturbations on global gene expression. A major source of noise in scRNA-seq CRISPR screens are ambient gRNAs, which are contaminating gRNAs that likely originate from other cells. If not properly filtered, ambient gRNAs can result in an excess of false positive gRNA assignments. Here, we utilize CRISPR barnyard assays to characterize ambient gRNA noise in single-cell CRISPR screens. We use these datasets to develop and train CLEANSER, a mixture model that identifies and filters ambient gRNA noise. This model takes advantage of the bimodal distribution between native and ambient gRNAs and includes both gRNA and cell-specific normalization parameters, correcting for confounding technical factors that affect individual gRNAs and cells. The output of CLEANSER is the probability that a gRNA-cell assignment is in the native distribution over the ambient distribution. We find that ambient gRNA filtering methods impact differential gene expression analysis outcomes and that CLEANSER outperforms alternate approaches by increasing gRNA-cell assignment accuracy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/611293v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@165c63dorg.highwire.dtl.DTLVardef@ba0e15org.highwire.dtl.DTLVardef@f2b12eorg.highwire.dtl.DTLVardef@14e6c86_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Bayesian Estimation of Allele-Specific Expression in the Presence of Phasing Uncertainty

MotivationAllele-specific expression (ASE) analyses aim to detect imbalanced expression of maternal versus paternal copies of an autosomal gene. Such allelic imbalance can result from a variety of cis-acting causes, including disruptive mutations within one copy of a gene that impact the stability of transcripts, as well as regulatory variants outside the gene that impact transcription initiation. Current methods for ASE estimation suffer from a number of shortcomings, such as relying on only one variant within a gene, assuming perfect phasing information across multiple variants within a gene, or failing to account for alignment biases and possible genotyping errors. ResultsWe developed BEASTIE, a Bayesian hierarchical model designed for precise ASE quantification at the gene level, based on given genotypes and RNA-Seq data. BEASTIE addresses the complexities of allelic mapping bias, genotyping error, and phasing errors by incorporating empirical phasing error rates derived from Genome-in-a-Bottle individual NA12878. BEASTIE surpasses existing methods in accuracy, especially in scenarios with high phasing errors. This improvement is critical for identifying rare genetic variants often obscured by such errors. Through rigorous validation on simulated data and application to real data from the 1000 Genomes Project, we establish the robustness of BEASTIE. These findings underscore the value of BEASTIE in revealing patterns of ASE across gene sets and pathways. Availability and ImplementationThe software is freely available from https://github.com/x811zou/BEASTIE. BEASTIE is available as Python source code and as a Docker image. Supplementary informationAdditional information is available online.

bioinformatics↗

bioRxiv2020

Detecting regulatory elements in high-throughput reporter assays

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure genome-wide regulatory element activity across the human genome. The assays, however, also present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq signals. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local signal correlations.

bioinformatics↗

bioRxiv2019

Scalable method for groupwise comparisons and its application to chromatin accessibility among primates

Changes in transcriptional regulation are thought to be a major contributor to the evolution of phenotypic traits, but the contribution of changes in chromatin accessibility to the evolution of gene expression remains almost entirely unknown. To address this important gap in knowledge, we developed a new method to identify DNase I Hypersensitive (DHS) sites with differential chromatin accessibility between species using a joint modeling approach. Our method overcomes several limitations inherent to conventional threshold-based pairwise comparisons that become increasingly apparent as the number of species analyzed rises. Our approach employs a single quantitative test which is more sensitive than existing pairwise methods. To illustrate, we applied our joint approach to DHS sites in fibroblast cells from five primates (human, chimpanzee, gorilla, orangutan, and rhesus macaque). We identified 89,744 DHS sites, of which 41% are identified as differential between species using the joint model compared with 33% using the conventional pairwise approach. The joint model provides a principled approach to distinguishing single from multiple chromatin accessibility changes among species. We found that non differential DHS sites are enriched for nucleotide conservation. Differential DHS sites with decreased chromatin accessibility relative to rhesus macaque occur more commonly near transcription start sites (TSS), while those with increased chromatin accessibility occur more commonly distal to TSS. Further, differential DHS sites near TSS are less cell type-specific than more distal regulatory elements. Taken together, these results point to distinct classes of DHS sites, each with distinct characteristics of selection, genomic location, and cell type specificity.

evolutionary biology↗